意见领袖的检测：在线学习社区中的静态评估与动态评估
Detection of opinion leaders: Static vs. dynamic evaluation in online learning communities.
作者信息
Kang Min, Liang Tian, Sun Bing, Mao Hong-Ying
机构信息
School of Economics and Management, Harbin Engineering University, Harbin, China.
School of Foreign Studies, Harbin Engineering University, Harbin, China.
出版信息
Heliyon. 2023 Mar 24;9(4):e14844. doi: 10.1016/j.heliyon.2023.e14844. eCollection 2023 Apr.
Opinion leaders play a critical role in maintaining the functioning of online communities. This study aims to detect opinion leaders in online learning communities by evaluating the influence of users within the community. We use Baidu Post Bar's Python learning community as an example and employ the catastrophe progression method to statically evaluate the influence of users in three dimensions: user creativity, user posting quality, and user online interaction. Based on this, we introduce the dual-incentive control line to dynamically evaluate users' influence from 2016 to 2020 regarding speed change characteristics, thus scientifically detecting opinion leaders in online learning communities. Compared to the static evaluation method, the results show that our proposed dynamic evaluation method can more effectively reveal the dynamic development trend of users' influence, thus accurately detecting opinion leaders. Moreover, this "invisible" development trend is fully reflected in the setting of the dual-incentive control line.
意见领袖在维持在线社区的运转方面发挥着关键作用。本研究旨在通过评估社区内用户的影响力来检测在线学习社区中的意见领袖。我们以百度贴吧的Python学习社区为例，采用突变级数法从用户创造力、用户发帖质量和用户在线互动三个维度对用户影响力进行静态评估。在此基础上，引入双激励控制线，从速度变化特征方面动态评估2016年至2020年用户的影响力，从而科学地检测在线学习社区中的意见领袖。与静态评估方法相比，结果表明我们提出的动态评估方法能够更有效地揭示用户影响力的动态发展趋势，从而准确地检测出意见领袖。此外，这种“隐形”的发展趋势在双激励控制线的设置中得到了充分体现。
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